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For: Mostafaei S, Abdollahi H, Kazempour Dehkordi S, Shiri I, Razzaghdoust A, Zoljalali Moghaddam SH, Saadipoor A, Koosha F, Cheraghi S, Mahdavi SR. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm. Radiol Med 2020;125:87-97. [PMID: 31552555 DOI: 10.1007/s11547-019-01082-0] [Cited by in Crossref: 17] [Cited by in F6Publishing: 27] [Article Influence: 5.7] [Reference Citation Analysis]
Number Citing Articles
1 Dumancas GG, Ellis H. Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory. Spectrochim Acta A Mol Biomol Spectrosc 2022;276:121186. [PMID: 35405374 DOI: 10.1016/j.saa.2022.121186] [Reference Citation Analysis]
2 Dumancas G, Adrianto I. A stacked regression ensemble approach for the quantitative determination of biomass feedstock compositions using near infrared spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2022;276:121231. [PMID: 35427923 DOI: 10.1016/j.saa.2022.121231] [Reference Citation Analysis]
3 Abdalvand N, Sadeghi M, Mahdavi SR, Abdollahi H, Qasempour Y, Mohammadian F, Birgani MJT, Hosseini K. Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters. Brachytherapy 2022:S1538-4721(22)00113-1. [PMID: 35933272 DOI: 10.1016/j.brachy.2022.06.007] [Reference Citation Analysis]
4 Nardone V, Reginelli A, Grassi R, Vacca G, Giacobbe G, Angrisani A, Clemente A, Danti G, Correale P, Carbone SF, Pirtoli L, Bianchi L, Vanzulli A, Guida C, Grassi R, Cappabianca S. Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022;14:3004. [PMID: 35740669 DOI: 10.3390/cancers14123004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Pisani C, Galla A, Loi G, Beldì D, Krengli M. Urinary toxicity in patients treated with radical EBRT for prostate cancer: Analysis of predictive factors in an historical series. Bulletin du Cancer 2022. [DOI: 10.1016/j.bulcan.2022.03.011] [Reference Citation Analysis]
6 Abdali SH, Afzali F, Baseri S, Abdalvand N, Abdollahi H. Bone radiomics reproducibility: a three-centered study on the impacts of image contrast, edge enhancement, and latitude variations. Phys Eng Sci Med 2022. [PMID: 35389137 DOI: 10.1007/s13246-022-01116-4] [Reference Citation Analysis]
7 Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022;42:1-13. [PMID: 35671432 DOI: 10.1200/EDBK_350931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Franzese C, Cozzi L, Badalamenti M, Baldaccini D, D’agostino G, Fogliata A, Navarria P, Franceschini D, Comito T, Clerici E, Reggiori G, Tomatis S, Scorsetti M. Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy. Strahlenther Onkol. [DOI: 10.1007/s00066-021-01886-y] [Reference Citation Analysis]
10 Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022;14:17562872221109020. [PMID: 35814914 DOI: 10.1177/17562872221109020] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
11 Amini M, Hajianfar G, Hadadi Avval A, Nazari M, Deevband MR, Oveisi M, Shiri I, Zaidi H. Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm. Clin Oncol (R Coll Radiol) 2021:S0936-6555(21)00433-7. [PMID: 34872823 DOI: 10.1016/j.clon.2021.11.014] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
12 Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021;169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Ferro M, de Cobelli O, Vartolomei MD, Lucarelli G, Crocetto F, Barone B, Sciarra A, Del Giudice F, Muto M, Maggi M, Carrieri G, Busetto GM, Falagario U, Terracciano D, Cormio L, Musi G, Tataru OS. Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021;22:9971. [PMID: 34576134 DOI: 10.3390/ijms22189971] [Cited by in Crossref: 1] [Cited by in F6Publishing: 22] [Article Influence: 1.0] [Reference Citation Analysis]
14 Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021;11:8027-42. [PMID: 34335978 DOI: 10.7150/thno.61207] [Cited by in Crossref: 6] [Cited by in F6Publishing: 10] [Article Influence: 6.0] [Reference Citation Analysis]
15 Albano D, Benenati M, Bruno A, Bruno F, Calandri M, Caruso D, Cozzi D, De Robertis R, Gentili F, Grazzini I, Micci G, Palmisano A, Pessina C, Scalise P, Vernuccio F, Barile A, Miele V, Grassi R, Messina C; Young SIRM Working Group. Imaging side effects and complications of chemotherapy and radiation therapy: a pictorial review from head to toe. Insights Imaging 2021;12:76. [PMID: 34114094 DOI: 10.1186/s13244-021-01017-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 8] [Article Influence: 1.0] [Reference Citation Analysis]
16 Shayesteh S, Nazari M, Salahshour A, Sandoughdaran S, Hajianfar G, Khateri M, Yaghobi Joybari A, Jozian F, Fatehi Feyzabad SH, Arabi H, Shiri I, Zaidi H. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer. Med Phys 2021;48:3691-701. [PMID: 33894058 DOI: 10.1002/mp.14896] [Cited by in Crossref: 5] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
17 Michalet M, Azria D, Tardieu M, Tibermacine H, Nougaret S. Radiomics in radiation oncology for gynecological malignancies: a review of literature. Br J Radiol 2021;94:20210032. [PMID: 33882246 DOI: 10.1259/bjr.20210032] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Ferini G, Pergolizzi S. A Ten-year-long Update on Radiation Proctitis Among Prostate Cancer Patients Treated With Curative External Beam Radiotherapy. In Vivo 2021;35:1379-91. [PMID: 33910815 DOI: 10.21873/invivo.12390] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
19 Amiri S, Akbarabadi M, Abdolali F, Nikoofar A, Esfahani AJ, Cheraghi S. Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models. Comput Biol Med 2021;133:104409. [PMID: 33940534 DOI: 10.1016/j.compbiomed.2021.104409] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Forde E, Leech M, Robert C, Herron E, Marignol L. Influence of inter-observer delineation variability on radiomic features of the parotid gland. Phys Med 2021;82:240-8. [PMID: 33677385 DOI: 10.1016/j.ejmp.2021.01.084] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021;132:104304. [PMID: 33691201 DOI: 10.1016/j.compbiomed.2021.104304] [Cited by in Crossref: 14] [Cited by in F6Publishing: 30] [Article Influence: 14.0] [Reference Citation Analysis]
22 Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2021;129:104135. [PMID: 33254045 DOI: 10.1016/j.compbiomed.2020.104135] [Cited by in Crossref: 11] [Cited by in F6Publishing: 20] [Article Influence: 5.5] [Reference Citation Analysis]
23 Yan L, Yao H, Long R, Wu L, Xia H, Li J, Liu Z, Liang C. A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma. Br J Radiol 2020;93:20200358. [PMID: 32960673 DOI: 10.1259/bjr.20200358] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 0.5] [Reference Citation Analysis]
24 Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020;10:1708. [PMID: 33117669 DOI: 10.3389/fonc.2020.01708] [Cited by in Crossref: 5] [Cited by in F6Publishing: 14] [Article Influence: 2.5] [Reference Citation Analysis]
25 Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020;47:4265-80. [PMID: 32615647 DOI: 10.1002/mp.14368] [Cited by in Crossref: 22] [Cited by in F6Publishing: 28] [Article Influence: 11.0] [Reference Citation Analysis]
26 Isaksson LJ, Pepa M, Zaffaroni M, Marvaso G, Alterio D, Volpe S, Corrao G, Augugliaro M, Starzyńska A, Leonardi MC, Orecchia R, Jereczek-Fossa BA. Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy. Front Oncol 2020;10:790. [PMID: 32582539 DOI: 10.3389/fonc.2020.00790] [Cited by in F6Publishing: 22] [Reference Citation Analysis]
27 Rastegar S, Beigi J, Saeedi E, Shiri I, Qasempour Y, Rezaei M, Abdollahi H. Radiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size. J Med Imaging Radiat Sci 2020;51:128-36. [PMID: 32089514 DOI: 10.1016/j.jmir.2019.11.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]